personalized nutrition by prediction of glycemic responsesrshamir/seminar/16/ppgr prediction... ·...
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Personalized Nutrition by Prediction of Glycemic Responses
David Zeevi, Tal Korem, Niv Zmora, David Israeli, Daphna Rothschild, Adina Weinberger, Orly Ben-Yacov, Dar Lador, Tali Avnit-Sagi, Maya Lotan-Pompan, Jotham Suez, Jemal Ali Mahdi, Elad Matot, Gal Malka, Noa Kosower, Michal Rein, Gili Zilberman-Schapira, Lenka
Dohnalova, Meirav Pevsner-Fischer, Rony Bikovsky, Zamir Halpern, Eran Elinav and Eran Segal
Nomi Hadar, 27.12.16
Background Meal response predictor
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Glycemic responses Motivation Main cohort
• The glycemic response to a food is the effect that food has on blood sugar (glucose) levels after consumption.
• A low glycemic food will release glucose more slowly and steadily, which leads to lower postprandial blood glucose readings.
• A high glycemic food causes a more rapid rise in blood glucose levels after meals.
• PPGRs = postprandial glycemic responses
Background Meal response predictor
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Glycemic responses Motivation Main cohort
• Blood glucose levels are rapidly increasing in the population, as evident by the sharp incline in the prevalence of prediabetes.
• Prediabetes, characterized by chronically impaired blood glucose responses, is a significant risk factor for type II diabetes.
• Maintaining normal blood glucose levels is critical for preventing and controlling diabetes and many other diseases.
Background Meal response predictor
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Glycemic responses Motivation Main cohort
• Dietary intake is a central determinant of blood glucose levels.
• In order to achieve normal glucose levels, it is imperative to make food choices that induce normal postprandial glycemic responses.
• Despite their importance, no method exists for predicting PPGRs to food.
Background Meal response predictor
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Glycemic responses Motivation Main cohort
• The current practice is to use the meal carbohydrate content, even though it is a poor predictor of the PPGR.
• Other methods: glycemic index, glycemic load.
• Ascribing a single PPGR to each food assumes that the response is solely an intrinsic property of the consumed food.
Background Meal response predictor
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Glycemic responses Motivation Main cohort
• However, few small-scale studies found high variability in PPGRs of different people to the same food.
• Factors that may affect interpersonal differences in PPGRs:
o Genetics. o Lifestyle. o Insulin sensitivity. o Propensity for obesity o Gut microbiota (little known). o And more.
Background Meal response predictor
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Glycemic responses Motivation Main cohort
Goals of study • To quantitatively measure individualized PPGRs,
characterize their variability across people and identify factors associated with this variability.
• Devised a machine learning algorithm that predicts personalized PPGRs.
Background Meal response predictor
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Glycemic responses Motivation Main cohort
continuous glucose monitoring (CGM)
The researchers continuously monitored glucose levels during an entire week in a cohort of 800 healthy and prediabetic individuals.
Background Meal response predictor
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Glycemic responses Motivation Main cohort
Main cohort: 800 healthy and prediabetic individuals
Background Meal response predictor
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Glycemic responses Motivation Main cohort
Main cohort: 800 healthy and prediabetic individuals
Background Meal response predictor
Results
Glycemic responses Motivation Main cohort
Main cohort: 800 healthy and prediabetic individuals
Background Meal response predictor
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Glycemic responses Motivation Main cohort
PPGRs associate with risk factors. Shown are PPGRs, BMI, HbA1c%, age, and wakeup glucose of all participants, sorted by median standardized meal PPGR (top, red dots). Correlation of factors with the median PPGRs to standardized meals is shown along with a moving average line. Moving average line = series of averages of different subsets of the full data set.
Background Meal response predictor
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Glycemic responses Motivation Main cohort
Kernel density estimation (KDE) smoothed histogram of the PPGR to four types of standardized meals provided to participants. Dashed lines represent histogram modes.
Kernel density estimation (KDE) = A technique to estimate the unknown probability distribution of a random variable, based on a sample of points taken from that distribution.
Background Meal response predictor
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Glycemic responses Motivation Main cohort
Example of high interpersonal variability and low intra-personal variability in the PPGR to bread across four participants (two replicates per participant consumed on two different mornings).
Background Meal response predictor
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Glycemic responses Motivation Main cohort
Example of two replicates of the PPGR to two standardized meals (left) / real-life meals (right) for two participants exhibiting reproducible yet opposite PPGRs.
So how should we know which food is the best for us in terms of glycemic response?
Background Meal response predictor
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General scheme Regression trees Gradient boosting regression Partial dependence plots
Prediction of Personalized Postprandial Glycemic Responses
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Prediction of Personalized Postprandial Glycemic Responses
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Prediction of Personalized Postprandial Glycemic Responses
Regression trees - intro • Decision tree is a predictive model
which maps observations about an item (the branches) to conclusions about the item's target value (the leaves).
• Classification trees: target variable is categorical.
• Regression trees: target variable is continuous.
Background Meal response predictor
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General scheme Regression trees Gradient boosting regression Partial dependence plots
CART (Classification And Regression Tree) algorithm
CART is a term to refer to decision tree algorithms that can used for classification or regression predictive modeling problems.
The main elements of CART are: 1. Rules for splitting data at a node based on the value of one
variable. 2. Stopping rules for deciding when a branch is terminal and
can be split no more. 3. Finally, a prediction for the target variable in each terminal
node.
Background Meal response predictor
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General scheme Regression trees Gradient boosting regression Partial dependence plots
Breiman et al.
Regression trees - CART algorithm outline • The tree is built through binary recursive partitioning. • Initially, all records in the training set are allocated into the first
two partitions or branches, using every possible binary split on every field.
• The algorithm selects the split that minimizes the sum of the squared deviations from the mean in the two separate partitions.
• This splitting rule is then applied to each of the new branches. • This process continues until each node reaches a user-specified
minimum node size and becomes a terminal node.
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Regression trees - CART algorithm • Finding the best binary partition in terms of minimum sum of squares
is generally computationally infeasible.
• Hence we proceed with a greedy algorithm.
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Regression trees - CART algorithm cont. • For each splitting variable, the determination of the split point s can
be done very quickly and hence by scanning through all of the inputs, determination of the best pair (j, s) is feasible.
• Having found the best split, we partition the data into the two resulting regions and repeat the splitting process on each of the two regions.
Background Meal response predictor
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General scheme Regression trees Gradient boosting regression Partial dependence plots
Regression trees - CART algorithm cont. • How large should we grow the tree? A very
large tree might overfit the data, while a small tree might not capture the important structure.
• Find sub-tree of that has the optimal trade-off of accuracy and complexity (the cross-validation is used to finding this sub-tree).
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
The Elements of Statistical Learning, Friedman
“Inside every big tree is a small, perfect tree waiting
to come out.” - Dan Steinberg
Is the regression tree a strong learner?
Background Meal response predictor
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The origin of boosting • The idea of boosting came out of the idea of whether a weak
learner can be modified to become better.
• A weak learner is defined as one whose performance is at least slightly better than random chance.
• The idea is to use the weak learning method several times to get a succession of hypotheses, each one refocused on the examples that the previous ones found difficult and misclassified.
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Gradient Boosting • AdaBoost (Adaptive Boosting) was the first boosting algorithm.
Gradient boosting generalizes it. Gradient boosting involves three elements: 1. A loss function to be optimized. 2. A weak learner to make predictions. 3. An additive model to add weak learners to minimize the loss
function.
Background Meal response predictor
Results
General scheme Regression trees Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Friedman (2001)
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Meal response predictor
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Gradient boosting regression
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Gradient boosting regression
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Gradient boosting regression
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Partial dependence plots • An insight into the contribution of the different features in
the algorithm’s predictions.
• PDPs graphically visualize the marginal effect of a given feature on prediction outcome after accounting for the average effect of all other features.
• While this effect may be indicative of feature importance, it may also be misleading due to higher-order interactions. Nonetheless, PDPs are commonly used for knowledge discovery in large datasets such as this.
Background Gradient boosting regression
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
The Elements of Statistical Learning, Friedman
Background Gradient boosting regression
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Background Gradient boosting regression
Results
General scheme Regression tree Gradient boosting regression Partial dependence plots
Partial dependence plots
Background Meal response predictor
Results
Don’t forget to check your predicted glycemic response to sufganiot…
Happy Hanukkah!